Currently there is an increased interest in devices that can unobtrusively monitor physiological/lifestyle parameters, e.g. estimate the energy expenditure using accelerometers. Today many of such devices are intended for fitness/sports monitoring (e.g. Nike Fuelband, MIO Alpha), but it is expected that such type of monitoring will become a commodity.
In order to make such devices attractive, they must be comfortable to wear, which means that they should be small and light. Therefore, battery size/power is an important design constraint. The less frequent the device needs to be recharged, the more user friendly it will be.
Different strategies are known to reduce battery power, e.g. one approach is to limit the sampling frequency of the physiological/lifestyle parameters, requiring less processing power on the subsequent algorithms. Another approach is to sample at a higher sampling frequency, but only for a limited amount of time. Such an approach is e.g. described in US 2006/123905 A1. This interrupted way of sampling has the advantage that, depending on the processor type, the corresponding processor sampling the signal can effectively be set to sleep, requiring only a very limited amount of battery power.
One of the interesting parameters to monitor using a lifestyle monitoring device is the number of steps taken during a certain time period, e.g. during the day, or, more generally, the number of cycles of a periodic movement, which may be steps, jumps, swimming strokes, revolutions during cycling, etc. With existing algorithms for continuous 3D accelerometer signals the 3D accelerometer signal is first processed to a 1D signal in which the periodicity of the accelerometer signal is clearly visible. On this pre-processed signal a fixed or variable threshold is set, that sets a flag at each time instance that the threshold is superseded. Finally, the number of threshold passings is simply counted.
For continuous sampling this is a suitable approach. However, for discontinuous sampling strategies this approach can lead to problems. Since only a limited amount of data is available, the periodicity of the signal is hardly visible anymore. Setting the (adaptive) threshold correctly becomes an issue, since the context is not visible anymore. In addition, some form of intra/extrapolation needs to occur in order to estimate the number of steps.
Hence, there is a need for an approach that is suitable for more precisely counting the number of cycles of a periodic movement of a subject if only discontinuous accelerometer data are available, e.g. to save battery power during the acquisition of the accelerometer data.
US2006/174685A1 discloses a method and apparatus for counting the steps taken by a walker, where the method includes detecting an acceleration value generated by a step taken by a walker at every first stated interval, calculating a standard deviation of detected acceleration values at every second stated interval, determining a walking mode corresponding to the calculated standard deviation among first through Nth walking modes as a walking pattern of the walker, in which N is a positive integer that is larger than 1, checking if there is at least one absolute value that is larger than a threshold acceleration value corresponding to the determined walking mode among the absolute values of the detected acceleration values, and incrementing a count value as a step taken by the walker if there is at least one absolute value that is larger than the threshold acceleration value among the absolute values of the detected acceleration values.
U.S. Pat. No. 7,334,472B2 discloses a method for measuring quantity of exercise and an apparatus comprising an acceleration sensor for generating acceleration information by measuring the quantity of exercise according to user movement, sensor control unit for supplying power to the acceleration sensor and sampling the acceleration information generated from the acceleration sensor, a dynamic energy measurement unit for converting the sampled acceleration information into dynamic energy, comparing a local maximum value with a predetermined threshold value if an ascending gradient of the dynamic energy has the local maximum value exceeding a predetermined value and determining a user step if the local maximum value exceeds the predetermined threshold value, a calorie consumption measurement unit for calculating calorie consumption by analyzing an energy level of dynamic energy determined as a user step, a memory for storing information, and a display section for displaying information related to the number of steps and calorie consumption.